Resistant AI Transactions is an AI-powered overlay for existing transaction monitoring systems, designed to enhance fraud detection and anti-money laundering (AML) capabilities without replacing existing infrastructure. The product offers 80+ pre-built machine learning models developed by AI scientists and financial crime experts, deployable in days rather than months. Models can be applied incrementally — one risk typology at a time — to supplement or replace existing rule-based detection. Core detection capabilities include: - Real-time fraud prevention covering inbound and outbound fraud typologies - AML detection using ensembles of behavioral and anomaly detection models - Alert prioritization and contextualization to reduce analyst workload The system supports both supervised models (trained on customer-specific transactional data) and unsupervised models (covering anomaly detection, behavioral segmentation, and typology-specific patterns). It incorporates graph-based techniques to map money flows, transaction sequencing to detect velocity attacks, identity clustering to expose fraud rings, and threat propagation to trace connected accounts when fraud is identified. Explainability features are built in to support regulatory compliance and internal communication. The product draws on global threat intelligence including crime statistics, country risk data, and population-level patterns. Reported performance metrics include: 5x analyst productivity improvement, 3x risk coverage increase, and sub-100ms real-time response rates. Use cases addressed include AML, authorized push payment (APP) fraud, and buy-now-pay-later (BNPL) fraud.